A system for identifying likely defects with an asset within a fleet of assets comprises sensors to detect asset sensor data, user input devices/user output terminals associated with the plurality of assets, and a server device. The server device collects historical data comprising historical asset sensor data generated by the sensors and historical inspection report data from the user input devices. The server device further uses an algorithm to identify correlations in the historical data, obtains current asset sensor data generated by sensors on a given asset and/or obtains current inspection report data from a user input device associated with the given asset, calculates one or more likely defects with the asset based on the identified correlations, and based on the current asset sensor data and/or the current inspection report data, and sends the calculated likely defects to user output terminal associated with the given asset for display thereof.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A system for identifying likely defects with an asset within a fleet of assets, the system comprising: one or more sensors configured to detect asset sensor data of the fleet of assets; one or more user input devices associated with said fleet of assets configured to receive inspection report data of the fleet of assets; one or more user output terminals associated with said fleet of assets; and a server device, wherein the server device is configured to: collect historical data comprising: historical asset sensor data generated by the one or more sensors on the fleet of assets; and historical inspection report data from the one or more user input devices associated with the fleet of assets; train an algorithm to identify correlations between data points in the historical asset sensor data and data points in the historical inspection report data, to detect when certain asset sensor data in the historical data has occurred at the same time as certain inspection report data in the historical data; obtain current asset sensor data from the one or more sensors on a given asset within the fleet of assets; calculate, using the trained algorithm, one or more likely defects which may be present on the given asset based on the identified correlations in the historical data, and based on the obtained current asset sensor data, by using the identified correlations to determine when the current asset sensor data has occurred at the same time as certain inspection report data in the historical data; and send said one or more likely defects to a user output terminal associated with the given asset, wherein said user output terminal is configured to display an indication of said one or more likely defects.
2. The system of claim 1, wherein the user input device associated with the given asset comprises said user output terminal associated with the given asset.
3. The system of claim 1, wherein the server device is configured to: select a display template from a memory of the server device based on said one or more likely defects and send the display template to the user output terminal, wherein the display template prioritises the display of one or more checklist items in the inspection report data dependent on said one or more likely defects.
4. The system of claim 3, wherein the server device is configured to dynamically update the display template as new current inspection report data and/or current asset sensor data is received.
5. The system of claim 1, wherein the server device is configured to calculate a suggested action based on said one or more likely defects, and send the suggested action to the user output terminal, and wherein the user output terminal is configured to display the suggested action.
6. The system of claim 5, wherein the server device is further configured to calculate a priority list of suggested actions to be provided at the user output terminal based on said one or more likely defects.
7. The system of claim 5, wherein the suggested action is for the user to inspect one or more components on the asset and update the current inspection report data at the user input device.
8. The system of claim 1, wherein one or more of the sensors is a smart sensor, and the server device is configured to instruct a smart sensor associated with an asset component related to a likely defect to provide sensor data to the server device to verify the current condition of the asset component.
9. The system of claim 1, wherein the fleet of assets is a plurality of vehicles which are all of the same vehicle type.
10. The system of claim 1, wherein the asset sensor data comprises diagnostic trouble codes as specified by one or more asset manufacturers to identify asset defects.
11. The system of claim 1, wherein the server device is configured to group the asset sensor data according to an asset component to which the asset sensor data relates and group the inspection report data according to an asset component to which the inspection report data relates.
12. The system of claim 1, wherein the server device is configured to use the algorithm to identify correlations between historical asset sensor data and historical inspection report data.
13. The system of claim 1, wherein the server device is configured to use the algorithm to identify correlations within the historical inspection report data.
14. The system of claim 1, wherein the server device is configured to add current asset sensor data to the historical data to generate new historical data, and to train the algorithm to recognize correlations in the new historical data, wherein the algorithm comprises a learning algorithm.
15. The system of claim 1, wherein the server device is configured to use the algorithm to identify correlations between historical asset sensor data and historical inspection report data.
16. A system for identifying likely defects with an asset within a fleet of assets, the system comprising: one or more sensors configured to detect asset sensor data of the fleet of assets; one or more user input devices associated with said fleet of assets and configured to receive inspection report data of the fleet of assets; one or more output terminals associated with said fleet of assets; and a server device, wherein the server device is configured to: collect historical data comprising: historical asset sensor data generated by the one or more sensors on the fleet of assets; and historical inspection report data from the one or more user input devices associated with the fleet of assets; train an algorithm to identify correlations between data points in the historical asset sensor data and data points in the historical inspection report data, to detect when certain asset sensor data in the historical data has occurred at the same time as certain inspection report data in the historical data; obtain current inspection report data from a user input device associated with a given asset within the fleet of assets; calculate, using the trained algorithm, one or more likely defects which may be present on the given asset based on the identified correlations in the historical data, and based on the obtained inspection report data, by using the identified correlations to determine when the current inspection report data has occurred at the same time as certain asset sensor data in the historical data; and send said one or more likely defects to a user output terminal associated with the given asset, wherein said user output terminal is configured to display an indication of said one or more likely defects.
17. The system of claim 16, wherein the server device is configured to add current inspection report data to the historical data to generate new historical data, and to train the algorithm to recognize correlations in the new historical data, wherein the algorithm comprises a learning algorithm.
18. The system of claim 16, wherein the server device is configured to use the algorithm to identify correlations between historical asset sensor data and historical inspection report data.
19. A method for identifying likely defects with an asset within a fleet of assets, the fleet of assets comprising one or more sensors configured to detect asset sensor data, and each of the assets associated with one or more user input devices configured to receive inspection report data of the fleet of assets and with one or more user output terminals, the method comprising: collecting historical data comprising: historical asset sensor data generated by the one or more sensors on the fleet of assets; and historical inspection report data from the one or more user input devices associated with the fleet of assets; training an algorithm to identify correlations between data points in the historical asset sensor data and data points in the historical inspection report data, to detect when certain asset sensor data in the historical data has occurred at the same time as certain inspection report data in the historical data; obtaining current asset sensor data from the one or more sensors on a given asset within the fleet of assets; calculating, using the trained algorithm, one or more likely defects which may be present on the given asset based on the identified correlations in the historical data, and based on the obtained current asset sensor data, by using the identified correlations to determine when the current asset sensor data has occurred at the same time as certain inspection report data in the historical data; and sending said one or more likely defects to a user output terminal associated with the given asset, wherein said user output terminal is configured to display an indication of said one or more likely defects.
20. A method for identifying likely defects with an asset within a fleet of assets, the fleet of assets comprising one or more sensors configured to detect asset sensor data, and each of the assets associated with one or more user input devices configured to receive inspection report data of the fleet of assets and with one or more user output terminals, the method comprising: collecting historical data comprising: historical asset sensor data generated by the one or more sensors on the fleet of assets; and historical inspection report data from the one or more user input devices associated with the fleet of assets; training an algorithm to identify correlations between data points in the historical asset sensor data and data points in the historical inspection report data, to detect when certain asset sensor data in the historical data has occurred at the same time as certain inspection report data in the historical data; obtaining current asset sensor data from the one or more sensors on a given asset within the fleet of assets; calculating, using the trained algorithm, one or more likely defects which may be present on the given asset based on the identified correlations in the historical data, and based on the obtained current inspection report data, by using the identified correlations to determine when the current inspection report data has occurred at the same time as certain asset sensor data in the historical data; and sending said one or more likely defects to a user output terminal associated with the given asset, wherein said user output terminal is configured to display an indication of said one or more likely defects.
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August 23, 2022
April 15, 2025
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